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from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
#from datasets import load_dataset
from transformers import pipeline
from PIL import Image, ImageDraw, ImageFont
import gradio as gr
#ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
def object_classify(img1,img2):
feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b2-finetuned-ade-512-512")
model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b2-finetuned-ade-512-512")
object_detector = pipeline(task="image-segmentation", model = model, feature_extractor = feature_extractor)
#list of dictionaries
dict_obj1 = object_detector(img1)
dict_obj2 = object_detector(img2)
#list of object labels present in the image
objects_1=[]
objects_2=[]
#gets the label from each dictionary
for i in dict_obj1:
objects_1.append(i['label'])
for j in dict_obj2:
objects_2.append(j['label'])
#gets the uncommon elements from the 2 lists
missing_objects= list(set(objects_1)-set(objects_2))
missing_objects= *missing_objects, sep = ", "
return missing_objects
TITLE = 'Missing Items'
DESCRIPTION = 'Input two indoor pictures. First image being the original and second is one with the missing item/s'
EXAMPLES = [['Bedroom_1.jpg'],['Bedroom_2.jpg']]
INPUTS=[gr.inputs.Image(type = 'pil', label='Original Image'),gr.inputs.Image(type = 'pil', label='Second Image')]
OUTPUTS=gr.outputs.Textbox("Missing Item/s is/are: ")
interface=gr.Interface(object_classify,
INPUTS,
OUTPUTS,
examples = EXAMPLES,
title = TITLE,
description=DESCRIPTION, allow_flagging="never")
interface.launch() |